With the rapid development of digital media technology and computer technology, demands and expectations on such fields as computer vision, artificial intelligence and machine perception have become higher and higher. People not only want computers to recognize objects in images and provide precise locations of the objects, which are typical computer vision problems, but also expect computers to have a higher level of perceptual capability like the human visual system. Image aesthetic analysis, especially image aesthetic quality assessment, has gained more and more attention currently. Image aesthetic quality assessment is to use a computer to perform intelligent analysis so as to determine the aesthetic quality of an image. A conventional method for image aesthetic quality assessment only takes the image aesthetic quality assessment as an isolated task and manually designs characteristics or uses characteristics from deep network learning to assess the quality. All these features are affected by the subjective factor of aesthetic and the precision can hardly meet the user's requirement, either.
For the human visual system, image aesthetic quality assessment can hardly be considered as an independent task, but it is usually accompanied by some other visual perception tasks. For example, when people are going to assess the aesthetic quality of an image, they must have comprehended the content of the image, namely, they can tell the semantic information of what they are looking at. Meanwhile, the multi-task learning can enable learning of several relevant tasks simultaneously, and lots of researches have proved that multi-task learning can improve effects of some or all tasks.
In view of this, the present application is proposed.